Volume 6 Supplement 1
Localization of Eosinophilic Esophagitis from H&E stained images using multispectral imaging
© Bautista and Yagi; licensee BioMed Central Ltd. 2011
Published: 30 March 2011
This study is an initial investigation on the capability of multispectral imaging to capture subtle spectral information that would enable the automatic delineation between the eosinophilic esophagitis and other eosin stained tissue components, especially the RBCs. In the method, a principal component analysis (PCA) was performed on the spectral transmittance samples of the different tissue components, excluding however the transmittance samples of the eosinophilic esophagitis. From the average spectral error configuration of the eosinophilic esophagitis transmittance samples, i.e. the difference between the actual transmittance and the estimated transmittance using m PC vectors, we indentified two spectral bands by which we can localize the eosinophils. Initial results show the possibility of automatically localizing the eosinophilic esophagitis by utilizing spectral information.
Eosinophils are type of white blood cells that are important part of the immune system. They are present in small amount in the intestine and blood but not normally in the esophagus. Infiltration of eosinophils into the esophagus could result to conditions such as eosinophilic esophagitis (EE) and gastroesophageal reflux. Since eosinophilic esophagitis and gastroesophageal reflux exhibit similar clinical and histology features studies have been conducted to determine the distinguishing features between the two conditions [1–3] where the number of eosinophil infiltration was found to be one of the indicative features of eosinophilic esophagitis. In  pathologists followed three methods to evaluate the infiltration of eosinophils: (1) subjective evaluation of the presence of eosinophils in the entire hematoxylin and eosin (H&E) stained histology section where semi-quantitative scoring was applied; (2) the eosinophils were counted in 5 high power fields (HPF, x400) and the average was calculated; and (3) eosinophil density was evaluated by counting the eosinophils in the mucosa of the entire histological section and measuring the area. The consistency of the results in the subjective and manual approach of assessing the degree the eosinophils infiltration in the methods just mentioned can be improved if appropriate digital processing is applied to the images.
Using the spectral colour of an image pixel for automatic object classification or segmentation can be considered simple if only there is an obvious spectral colour difference between the objects of interest and the background objects. From an H&E stained tissue slide the eosinophils appear red to pink similar to other connective tissue components such as the red blood cells (RBC). Thus there is a challenge in using the RGB colour vector as feature variable for the classification or segmentation of eosinophils.
Multispectral imaging is popularly applied to remote sensing applications, but it has gained significant attentions from researchers in various fields. The technology has been studied for accurate colour reproduction , colour enhancement [5, 6], digital staining , and others. A multispectral imaging system employs more than 3 (N>3) narrowband filters which result to greater spectral sensitivity compared to the conventional RGB imaging system which utilizes 3 broadband filters. The capability of multispectral imaging to delineate tissue structures that are closely similar in their spectral colour have been shown in . In this paper we proposed a method to effectively visualize, detect and segment the eosinophils using information from the multispectral images of H&E stained tissue images, particularly using the spectral error between the original spectral transmittance of a pixel and its estimated transmittance which is calculated by using m PC vectors.
Materials and methods
The multispectral (MS) band numbers and their wavelength counterparts. The value of the spectral transmittance at MS band #1 corresponds to the average transmittance value between λ1 and λ2 nm.
Multispectral band #
450 - 465
470 - 485
490 - 505
510 - 525
530 - 545
550 - 565
570 - 585
590 - 605
610 - 625
630 - 645
650 - 665
670 - 685
690 - 705
710 - 720
where the entries of the Nx1 column vector f correspond to the spectral values at different wavelengths. We manually extracted the spectral samples for nucleus, cytoplasm, red blood cells (RBC), fiber, white area (the area in the image which is void of tissue) from the 5 images of the 10 sets of images that we captured. .
Principal component analysis (PCA)
where αi is the PC coefficient and vi is the ith eigenvector.
The reconstruction error that results from the application of eqn. 6 largely depends on the accurate estimation of the data covariance matrix C which in turn is governed by the sufficiency of the data samples in F. If the feature variance of a sample is captured in the data matrix F fewer eigenvectors are needed to obtain smaller error in the reconstruction of such sample feature.
where ek is an n dimensional column vector. The magnitude of the error in eqn.7 is a function of the estimation of .Consider that there are c classes of objects that are identified from an image but only c-1 of these classes are represented with spectral samples in the data matrix F, then for a given m eigenvectors the spectral errors are smaller for objects that belong to the first c-1 classes compared to objects that belong to the cth class.
Detection and segmentation using the spectral error
Although in an H&E stained slide the tissue structures are generally categorized as either acidophilic or basophilic each tissue structure has its own distinct spectral attributes due to its unique reactions to the chemical dyes. Hence the spectral error of a tissue component not represented in the data matrix F would likely exhibit peaks at certain wavelengths for a given m eigenvectors; these wavelengths might be correlated to the absorption peaks of the dyes themselves. With these specific wavelengths identified it is possible to detect and segment such particular tissue component by applying appropriate thresholds. Furthermore translating the spectral error values at these wavelengths would also result to better visualization of the tissue component.
The objective of this paper is to detect and segment the eosinophils from H&E stained esophagus tissue images. For the purpose of the experiment we collected 10 multispectral images from two different H&E stained slides.
Detection and Segmentation of the eosinophils
To segment the eosinophils the spectral errors at band 7 and band 10 were utilized. By implementing eqn. 8 and 9 where er correspond to the spectral error at band 10 and es to the error at band 7, the eosinophils were successfully segmented. Figure 6 shows the result of the segmentation after applying morphological filter, i.e. dilation and erosion (edge detection using the canny algorithm as implemented in Matlab was applied to the results before they are overlaid on the original RGB color images). Here we see that the method was able to delineate between eosinophils and other similarly stained tissue components, i.e.RBC. There are however few eosinophils that were not detected by the process and this can be because (i) the spectral training samples was insufficient; (ii) the bandwidth of each spectral band is not narrow enough to capture minute changes in the spectral colours; and/or (iii) the number of spectral bands is not enough to capture the subtle spectral difference between eosinophils and other eosin stained tissue structures.
Segmentation using RGB color information
In a stained tissue slides, tissue structures which differ in their functionality can be stained with the same dye. The eosinophils and red blood cells (RBC), for instance, are both stained with eosin dye which made them share similar color attributes, i.e. pink to red. We have introduced a simple way to detect and segment the eosinophils using multispectral information particularly from the spectral errors of the multispectral pixels. The utilization of spectral errors was first introduced in  to enhance abnormal skin areas. With modifications of the method the used of spectral error was applied to H&E stained images to improve the colorimetric difference between collagen and muscle fiber [6, 7]. Spectral error subtraction as a method to segment tissue components, which is currently being utilized to segment/detect eosinophils, was not addressed in both papers, however.
In this paper we have addressed the detection and segmentation of eosinophils from an H&E stained slide of an esophagus tissue using multispectral information, particularly using the spectral error difference between two specific bands that were identified from the plot of the spectral error itself. The results of the experiment show that with multispectral information it could be possible to classify tissue structures with very similar staining attributes which is difficult with the conventional RGB color information.
Quantification of eosinophils in H&E stained esophagus tissue images is helpful in identifying eosinophilic esophagitis from gastroesophageal reflux. The segmentation of eosinophils presented in this paper can be utilized as a first step in the quantification process. Moreover, the proposed method could also be useful to segment other tissue structures which are stained similarly by simply excluding the spectral samples of such structures in the derivation of the eigenvectors (PC vectors) that are used to estimate the spectral transmittance of a multispectral pixel.
The authors thank Dr. Hongki Yoo, Ph.D and Dr. Guillermo J. Tearney, M.D,Ph.D of the Wellman Center for Photomedicine at Massachusetts General Hospital, Boston ,USA for the H&E stained slides.
This article has been published as part of Diagnostic Pathology Volume 6 Supplement 1, 2011: Proceedings of the 10th European Congress on Telepathology and 4th International Congress on Virtual Microscopy. The full contents of the supplement are available online at http://www.diagnosticpathology.org/supplements/6/S1
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